Challenge 9: Baby Names

Setup

library(tidyverse)
library(knitr)
library(DT)

names_a <- read_csv(here::here('supporting_artifacts', 'StateNames_A.csv'))

Dataset Preview

datatable(names_a, class = 'cell-border stripe')

Warning in instance$preRenderHook(instance): It seems your data is too big
for client-side DataTables. You may consider server-side processing: https://
rstudio.github.io/DT/server.html

3 Summarizing and Visualizing

1.

names_allison <- names_a |> 
  filter(
    Name == 'Allison'
  ) |> 
  group_by(
    State,
    Gender
  ) |> 
  summarize(sum = sum(Count)) |> 
  pivot_wider(
    names_from = Gender,
    values_from = sum
  ) |> 
  mutate(
    M = replace_na(M, 0)
  ) |> 
  rename(
    "Number of Female Babies" = `F`,
    "Number of Male Babies" = M
  )

`summarise()` has grouped output by 'State'. You can override using the
`.groups` argument.

kable(names_allison, 'pipe')

State	Number of Female Babies	Number of Male Babies
AK	232	0
AL	1535	0
AR	1198	0
AZ	1880	0
CA	12413	0
CO	1594	0
CT	1099	0
DC	321	0
DE	294	0
FL	4455	0
GA	3257	0
HI	183	0
IA	1477	0
ID	451	0
IL	5110	0
IN	3067	0
KS	1283	0
KY	1905	20
LA	1209	0
MA	2218	0
MD	2229	0
ME	340	0
MI	4014	0
MN	2374	0
MO	2882	0
MS	817	0
MT	226	0
NC	3435	0
ND	285	0
NE	807	0
NH	412	0
NJ	3052	0
NM	399	0
NV	729	0
NY	5747	0
OH	5487	0
OK	1421	0
OR	1186	0
PA	4307	0
RI	306	0
SC	1228	0
SD	376	0
TN	2488	0
TX	10192	0
UT	1125	0
VA	3220	0
VT	135	0
WA	1956	0
WI	2367	0
WV	813	0
WY	142	0

datatable(names_allison, class = 'cell-border stripe')

2.

names_allison <- names_allison |> 
  select(
    State,
    `Number of Female Babies`
  )

3.

allison_years <- names_a |> 
  filter(
    Name == 'Allison'
  ) |> 
  group_by(
    Year
  ) |> 
  summarize(
    sum = sum(Count)
  )

allison_years |> ggplot(
  mapping = aes(x = Year, y = sum)
) + 
  geom_line() +
  labs(
    y = element_blank(),
    title = 'Frequency of Babies Named "Allison"'
  )

4 Modeling the Number of Allisons

4.

allison_model <- allison_years |> 
  lm(
    sum ~ Year,
    data = _
  )

5.

allison_model |> 
  ggplot(
    mapping = aes(x = Year, y = sum)
  ) +
  geom_point() +
  stat_smooth(method = 'lm') +
  labs(
    y = 'Number of Babies Named Allison'
  )

`geom_smooth()` using formula = 'y ~ x'

6.

predicted count_allison = 209815.1 - 101.6(Year)

7.

allison_model |> 
  broom::augment() |> 
  ggplot(
    mapping = aes(y = .resid, x = .fitted)
  ) +
  geom_point() +
  labs(
    x = 'Predicted',
    title = 'Residual Plot',
    y = 'Residuals'
  ) +
  geom_hline(
    yintercept = 0,
    linetype = 'dashed'
  )

There are no clear trends in the residual plot but there is a group of predicted counts from around 5900 to 6600 that were all underestimated.

8.

Your name is declining in coolness/popularity.

5 Spelling by State

1.

names_allan <- names_a |> 
  filter(
    Name == 'Allan' | Name == 'Alan' | Name == 'Allen'
  )

2.

names_allan <- names_allan |> 
  filter(
    State == 'CA' | State == 'PA',
    Year == '2000'
  ) |> 
  group_by(
    State,
    Name
  ) |> 
  summarize(
    sum = sum(Count)
  ) |> 
  pivot_wider(
    names_from = Name,
    values_from = sum
  ) |> 
  rename(
    'Frequency of "Alan"' = Alan,
    'Frequency of "Allan"' = Allan,
    'Frequency of "Allen"' = Allen
  )

`summarise()` has grouped output by 'State'. You can override using the
`.groups` argument.

kable(names_allan, 'pipe')

State	Frequency of “Alan”	Frequency of “Allan”	Frequency of “Allen”
CA	584	131	176
PA	51	12	56

3.

names_allan_pct <- names_allan |> 
  mutate(
    total = sum(
      `Frequency of "Alan"`, 
      `Frequency of "Allan"`, 
      `Frequency of "Allen"`
    ),
    `Percent "Alan"` = (`Frequency of "Alan"` / total) * 100,
    `Percent "Allan"` = (`Frequency of "Allan"` / total) * 100,
    `Percent "Allen"` = (`Frequency of "Allen"` / total) * 100
  ) |> 
  select(
    -total,
    -`Frequency of "Alan"`,
    -`Frequency of "Allan"`,
    -`Frequency of "Allen"`
  )

kable(names_allan_pct, 'pipe')

State	Percent “Alan”	Percent “Allan”	Percent “Allen”
CA	65.54433	14.70258	19.75309
PA	42.85714	10.08403	47.05882

In CA, 65.54% were named Alan, 14.7% were named Allan, and 19.75% were named Allen. In PA, 42.86% were named Alan, 10.08% were named Allan, and 47.06% were named Allen.